{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import brambox.boxes as bbb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "annotations = bbb.parse('anno_darknet', 'inria/Test/pos/yolo-labels/', class_label_map={0: 'person'}, image_width=1., image_height=1.)\n",
    "annotations = bbb.parse('anno_darknet', 'testing/clean/yolo-labels/', class_label_map={0: 'person'}, image_width=1., image_height=1.)\n",
    "patch_simen = bbb.parse('det_coco', 'patch_simen.json', class_label_map={0: 'person'})\n",
    "patch_up = bbb.parse('det_coco', 'patch_up.json', class_label_map={0: 'person'})\n",
    "clean_results = bbb.parse('det_coco', 'clean_results.json', class_label_map={0: 'person'})\n",
    "noise_results = bbb.parse('det_coco', 'noise_results.json', class_label_map={0: 'person'})\n",
    "class_results = bbb.parse('det_coco', 'class_shift.json', class_label_map={0: 'person'})\n",
    "class_only = bbb.parse('det_coco', 'class_only.json', class_label_map={0: 'person'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UukDEEBAOKRJRAZt9AimlAfgLsBn4A3OU0WkhxPtCiNLUv1eBp4UQJ4ClwBRp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\nChqNRqMx+P9H8hKNunW1DQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6972baa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "teddy = bbb.pr(patch_simen, annotations)['person']\n",
    "up = bbb.pr(patch_up, annotations)['person']\n",
    "noise = bbb.pr(noise_results, annotations)['person']\n",
    "clean = bbb.pr(clean_results, annotations)['person']\n",
    "class_shift = bbb.pr(class_results, annotations)['person']\n",
    "class_only_pr = bbb.pr(class_only, annotations)['person']\n",
    "\n",
    "\n",
    "\n",
    "#ap = bbb.ap(teddy[0], teddy[1])\n",
    "#plt.plot(teddy[1], teddy[0], label=f'Teddy: mAP: {round(ap*100, 2)}%')\n",
    "\n",
    "plt.plot([0, 1.05], [0, 1.05], '--', color='gray')\n",
    "\n",
    "ap = bbb.ap(clean[0], clean[1])\n",
    "plt.plot(clean[1], clean[0], label=f'CLEAN: AP: {round(ap*100, 2)}%')\n",
    "\n",
    "ap = bbb.ap(noise[0], noise[1])\n",
    "plt.plot(noise[1], noise[0], label=f'NOISE: AP: {round(ap*100, 2)}%')\n",
    "\n",
    "ap = bbb.ap(class_shift[0], class_shift[1])\n",
    "plt.plot(class_shift[1], class_shift[0], label=f'OBJ-CLS: AP: {round(ap*100, 2)}%')\n",
    "\n",
    "ap = bbb.ap(up[0], up[1])\n",
    "plt.plot(up[1], up[0], label=f'OBJ: AP: {round(ap*100, 2)}%')\n",
    "\n",
    "ap = bbb.ap(class_only_pr[0], class_only_pr[1])\n",
    "plt.plot(class_only_pr[1], class_only_pr[0], label=f'CLS: AP: {round(ap*100, 2)}%')\n",
    "\n",
    "#plt.gcf().suptitle('PR-curve')\n",
    "plt.gca().set_ylabel('Precision')\n",
    "plt.gca().set_xlabel('Recall')\n",
    "plt.gca().set_xlim([0, 1.05])\n",
    "plt.gca().set_ylim([0, 1.05])\n",
    "plt.gca().legend(loc=4)\n",
    "plt.savefig('pr-curve.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bR7VT3MJsNvP5558zY8YMpJQ8/PDDjg7JLlRSMJth83/gr/e16RsJcHYPrBwP974DocMd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\naGdgJ/czDq3RFBW0UyhBxMbGMmDAAEwmE1u3bs3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33Rk3bpzxQE6nZ8+e9OzZM6vsPPnk\nkzz22GMAuLi4GG+RXl5e7Nq1C7BujlS1alVq165NjRo1GDp0KFeuXDHeGNP3Y6hWrZoh2GevM0gn\nowx2vXr1eOWVV/jggw+YMGGCzbW9e/emd+/e7N69m4kTJ7J9+3Y6d+7MwYMH8fX1pVq1ajRv3txY\nN5Pu2MA6g27RokW8//77/Pzzz3Tq1Inhw4fnaFvTpk2NTYl27NjB4cOHefLJJwHrXiHVq1fnwIED\ntG7d2rguvW2uX79OYGAgv/32G0opkpOTc7xXTr8VwMWLFwkICODLL780egaJiYmULVuWQ4cO8c03\n3zB06FAbgcIFCxbQrVs3mxmDKSkpnD9/Hl9fXz7++GM+/vhj3njjDZYvX05AQAABAQEATJ06lVdf\nfZUffviBZcuWUatWLT766KNC3U9BP6HygcuXL/Paa68RHByM2Wxm9OjRlClTRjuELMjpjT6/cHd3\nN/bITSc+Pp7Y2FieeOIJGxXPnj170rdv37u6x0MPPWR8L1WqlHFcqlQpYww9ODiY6OhoY6eu+Ph4\n1q1bZzxMZ82ahb+//13d907S1UXT33779evHjBkzcszTunVrfv/9d/766y+qVq3Ku+++a8RbBgwY\ngJubW6brN2zYQOPGjblx4wanT59m9erVdOnShYEDB1KuXLls71O+fHnju4gQGBhos2/Dpk2bssw7\nceJE2rVrR0hICDExMffV+46Pj6d79+68//77NGvWzEivWbMmffr0AawOM324MSP79+8nPDycBQsW\ncOPGDZKSkqhQoQIffPAB5cqVM/I/++yzmZR4waqyGxERwaRJk2jTpg07d+5k+vTp7Nixg06dOt1z\nfe4XHVPIQ0SEr7/+moYNG7J27VqmTp3Kjz/+SJkyZQrbNE0GOnToQEJCAsuWLQOsiwfHjh3L4MGD\ns3yI7dmzJ88nBVgsFlavXs0vv/xiSCpv2LAh08Y6eYG9MtinTp1KF6fkyJEjJCYmUqVKFVJTUw0J\n8aioKKKiojKNnScnJzN79mzGjRvHrVu3jFhEamoqSUlJRERE2LW1ZIcOHVi7dq0xy+vKlSucPXuW\nZs2asXv3bs6cOWOkg7WnUKNGDcAaEL5XkpKS6N27N4MGDbJxwM8884zRqwsLC7NxhmANGsfGxhIT\nE8OHH37IoEGDmDFjBkopnn76aUJDQwFrTyg9dpLOxIkTjeGs9LYrNLnsDOhX1zwkNjaWIUOG0KhR\nI5YsWYKHh0dhm6TJAqUUISEhvPTSS0ybNg2LxUK3bt0yxQ3SYwoiwiOPPGLslpaSkmK89dszTp0d\n4eHh1KhRI1PQt3Xr1hw/fpyLFy9mmy+nmEKdOnWIj48nKSmJ9evXs3XrVtzd3Zk8eTKtW7fG0dER\nZ2dn4yGasax169axbNkyHB0dcXJyYtWqVcawTPrq+ooVK/LVV19l6vHOnz+fwMBAypUrh8lkIiEh\nAS8vL7p160alSpWIjY3Fyckp1/Zwd3dn+vTpdO7cGYvFgqOjI/Pnz6dZs2Z89tln9OnTB4vFQvXq\n1dm2bRvjxo0jMDCQ6dOn071791zLz+63Wr16Nbt37yYuLs5oly+++AKz2czbb7/NwIEDCQoKokKF\nCsbfQE5bqmZk5syZBAQEMHr0aKpVq8bnn39unPvpp58AjBjMgAED8PLyolatWjbbwxY0Kv3toKjQ\npEkTSd/H9UHAYrGwbds2unTpAvxvDYLWK8qeEydO2L1py4PGmDFjcHV1tdlwR5M1b775JgEBATnu\nl6zJe7L6H1NKHRaRJrnl1cNH98Fvv/1G+/bt6dq1K7t37waswTPtEIonfn5+REVFMXDgwMI2pcgw\na9Ys7RCKGHr46B5ISUkhKCiISZMm8dBDD7FkyRItYFcCuJ9tHjWaooJ2CvdAjx492LJlC7169WLB\nggVZLgbSaDSaooh2CnaSmJiIo6MjpUqVYtiwYQwdOpRnn31W6xVpNJpihY4p2MGBAwfw8fExVoL6\n+/vTr18/7RA0Gk2xQzuFHLh58yZjxozB19eX//73vzZL3DUajaa4oZ1CNoSHh+Pl5cXs2bP5xz/+\nwdGjR+natWthm6XJI/744w/69++Pi4sLjRs3plu3bpw8eZKYmBg8PT1trj9w4ABPPfUUZrOZhg0b\nMmXKFLvus379epRSREf/T98pJiYGJycnzGYz7u7ujBw5EovFkmM5rVq1wmw2Yzabefzxx22USA8e\nPEjp0qVtVmqnk52M8xdffEG1atWMstPn3v/66680btwYk8lkyHanpKTQsWPHQl1cNXjwYKOObdu2\nJbfp6WPGjDHq5ubmRqVKlQDYtWuXkW42mylbtmyWkt/ZtQ/AuHHj8PDwoGHDhrz66quICImJiXTt\n2hVPT09DGh9gxIgRHDlyJC+aIN/RMYVsSElJwdHRkbCwMBtJXE3RRkTo3bs3gYGBrFy5ErAK2P35\n55/UqlUryzyBgYGsXr0ab29vUlNT7VblDA4OpmXLlgQHB2dSEHVxcSEyMpKUlBTat2/P+vXrDUmE\nrMioudO3b1969eplHKempvLWW2/ZqHRmJKOMs8ViMVYGAzz33HM2ctqLFi1izpw51KlTh9dee411\n69axcOFC/v73v+coXZEVKSkphSbxEhQUZHyfN2+esWisXbt2hgTIlStXeOKJJ7Jtv6zaZ9++fezd\nu5eoqCgAWrZsSVhYGPHx8bRs2ZLx48fTokULXnrpJX7++WdSU1PvSiywMNE9hQysX7/e0F5p164d\nx44d0w6hGLJr1y4cHR0zrQr29vbOcVrxpUuXDIE7BwcHG8mCrLhx4wZ79uxhyZIlhvO5k9KlS+Pr\n65urNHY68fHx7Ny5M1NPYd68efTt2zdbaWfIXsY5OxwdHUlISDDkna9du8amTZvskqwA6xv9yJEj\neeqppxg3bhw3b95k6NChNG3alEaNGhky4ampqbzxxht4enpiMpmYN28eYBWKe/LJJ/H09GTEiBHk\nxSLb4OBgnn/+eZv0tWvX4ufnd1fOTinF7du3SUpKIjExkeTkZB599FGj3ZKTkw2bJ06cyLRp0+7b\n/oJC9xSAP//8k1deeYU1a9bg4+PD2LFjtYBdARG++iR/nbuRp2VWrVWBVv1sdWrSOXr0qI10dm6M\nGTOG+vXr07ZtW7p27UpgYKChoJmd5MGGDRvo2rUrbm5uVKlShcOHD9vcNyEhgR07dhjyC2az2XiD\nzYr169fToUMHKlasCMCFCxcICQlh165dHDx4MMs8Ock4A6xbt47du3fj5uZGUFAQtWrV4uWXX2bQ\noEEkJiayaNEipk2bxvjx4+9KvfP8+fPs27cPBwcHxo8fT/v27Vm6dCnXrl2jadOmdOzYkWXLlhET\nE0NkZCSlS5c2ejCjRo1i0qRJAAQEBPDtt9/y9NNPZ3uvYcOGMXLkSJo0yXrB7tmzZzlz5gzt27e3\nObdy5Upef/31bMvOqn2aN29Ou3bteOyxxxARRo0aRcOGDXF1dWX58uU0a9aMN998k40bN+Lj41Ok\npq2X6J6CiLB8+XLc3d3ZsGED77//PgcOHNACdhobJk2axKFDh+jcuTNff/21EV9q0qRJtho4wcHB\nxm5d/fv3zyR2d/r0acxmMy1atKB79+7Ghks5OYT0MjO+7Y4ePZqZM2fm+LDOKON85MgRmjdvzhtv\nvAFYJa9jYmKIioqiU6dOBAYGAlC7dm1CQ0PZv38/5cqV4/z58zRs2JCAgACee+45Tp48mVuT8eyz\nzxqr+7du3cqMNm3YNgAAC6xJREFUGTMwm820bduW27dvExsby/bt23nxxReNF7B0aexdu3bx1FNP\n4eXlxc6dOzl27FiO91q8eHG2DgGsD35/f38btYGLFy/yyy+/GDI1d5Jd+5w6dYoTJ05w/vx5Lly4\nwM6dO439Hb7++mt++uknnn32WWbPns3YsWN5/fXX8ff3Z+PGjbm2W6GTvjNTUfk0btxY8oqYmBgp\nU6aM+Pr6yokTJ/KsXE3OHD9+vFDvv337dmnVqlWW586cOSMeHh455k9OTpZKlSrJX3/9le01cXFx\n4uTkJLVr1xZnZ2epWbOm1KpVSywWi133yIrLly9L5cqV5datW0ZanTp1xNnZWZydnaV8+fJSrVo1\nCQkJyZTPYrFIuXLlJDU1VUREYmNjxd3d3ab8lJQUqVixok16v3795OTJkzJ+/HgJDQ2VmJgYGTBg\nQI62BgYGypo1a4xjHx8fiY6OtrmuT58+snXr1kxpt27dkurVq0tsbKyIiEyePFkmT55sU26bNm3k\n4MGDOdqRjtlslr1799qkz549W4YPH25XGRnb51//+pdMnTrVOPfee+/JzJkzbcr+/PPPZevWrTJl\nyhRJSUmR1q1b23Wv+yWr/zHgkNjxjC1xPQWLxWLIFTg7O7N37152795NgwYNCtkyTUHRvn17EhMT\n+eyzz4y0qKgomw1UMvLdd98ZY8S//fYbDg4OxkyWrFi7di0BAQGcPXuWmJgYzp07R926dXO8R26s\nXbuWHj16ZNpy88yZM4b0tr+/PwsWLLCZmZSTjHNGRdaNGzfaiKiFhYXx+OOP4+rqSkJCAqVKlcok\n7/zOO+8QEhKSq+1dunRh3rx5RhumB3w7derEokWLjD0mrly5wu3btwGoWrUqN27cyHZGlb1ER0dz\n9epVmjdvbnMuuzhDOtm1T+3atQkLCyMlJYXk5GTCwsIytd3Vq1f59ttvGTRokNFuSilu3bp1X3Up\nCEqUUzh58iRt27alW7duhIWFAdbuvxawK1mkS2dv374dFxcXPDw8eOedd/i///s/wDods2bNmsZn\nzZo1LF++nPr162M2mwkICGDFihU4ODhw6NAhY5P7jAQHB9O7d+9MaX379s11vwSz2ZztuZUrV+b4\nAMuprJkzZzJlyhRMJhPLly/no48+AmDu3Ll4eHjg7e3N3LlzM+1NICJMnz7d2FB+xIgRvPbaa3Tv\n3t0Yfvrll1+MdsuJiRMnkpycjMlkwsPDwyhz2LBh1K5dG5PJhLe3N19//TWVKlVi+PDheHp60qVL\nF2M3tpwYNmxYttNTV65cSf/+/W0Wm6Y76zZt2mRKnzRpkjHMk137+Pv74+LigpeXF97e3nh7e2eK\neUydOpV3332XUqVK0aVLF2OKe/qOaw8yJUI6OyUlhY8++ojJkyfj5OREUFAQgYGBekVyIVGUpbM1\nmenSpcsDtb+wxsr9SGeXiOk13bt3Z+vWrfTp04f58+fb9Waj0WhyRzuE4kexdQq3b9/G0dERBwcH\nRowYwYgRI+56n12NRqMpaRTLmMLevXsxm82GgF3fvn21Q9BoNBo7KFZO4caNG7z66qu0atWK27dv\n63HrB5iiFsvSaIoK9/u/VWycQlhYGJ6ennzyySeMGjWKo0eP0qlTp8I2S5MFZcuWJS4uTjsGjSaP\nERHi4uIyTVu+W4pVTKFcuXKEh4fTokWLwjZFkwM1a9bk/PnzXL58ubBN0WiKHWXLlqVmzZr3nD9f\np6QqpboCcwAHYLGIzLjj/EPAMqAxEAc8JyIxOZWZcUrqN998Q3R0NOPHjwes4lp6zYFGo9HYYu+U\n1HwbPlJKOQDzAT/AHXheKXWntOQLwFUReQIIAmbaU/Yff/yBv78/ffv2JSQkhKSkJADtEDQajeY+\nyc+YQlPglIj8LiJJwEqg1x3X9AK+TPu+FuigcllRFhcXR8OGDfn222/54IMP2Ldvnxaw02g0mjwi\nP2MKNYBzGY7PA09ld42IpCilrgNVgL+yK/Ts2bO0aNGCxYsXU79+/Tw2WaPRaEo2RSLQrJQaAYxI\nO0zcs2fP0RIsYFeVHJxmCaAk178k1x10/e+3/s72XJSfTuECkHFvw5ppaVldc14pVRp4BGvAORMi\n8hnwGYBS6pA9wZLiiq5/ya1/Sa476PoXVP3zM6ZwEHBVStVVSpUB+gN37jCxEQhM++4P7BQ9eV2j\n0WgKjXzrKaTFCEYBW7BOSV0qIseUUlOxbvawEVgCLFdKnQKuYHUcGo1Goykk8jWmICLfA9/fkTYp\nw/fbwLN3WexnuV9SrNH1L7mU5LqDrn+B1L/I7aeg0Wg0mvyj2GgfaTQajeb+eWCdglKqq1LqV6XU\nKaXU21mcf0gptSrt/I9KqToFb2X+YUf9X1dKHVdKRSmldiil7JpuVhTIre4ZruurlBKlVLGakWJP\n/ZVS/dJ+/2NKqa8L2sb8xI6//dpKqV1KqZ/S/v67FYad+YFSaqlS6pJS6mg255VSam5a20QppXzy\n3AgReeA+WAPTp4F6QBngZ8D9jmteAj5N+94fWFXYdhdw/dsB5dK+/6O41N+euqdd9zCwGzgANCls\nuwv4t3cFfgL+lnZcvbDtLuD6fwb8I+27OxBT2HbnYf1bAz7A0WzOdwN+ABTQDPgxr214UHsK+SKR\nUYTItf4isktEEtIOD2BdB1IcsOe3B5iGVSvrdkEaVwDYU//hwHwRuQogIpcK2Mb8xJ76C1Ax7fsj\nwH8K0L58RUR2Y52JmR29gGVi5QBQSSn1WF7a8KA6hawkMmpkd42IpADpEhnFAXvqn5EXsL49FAdy\nrXtal7mWiHxXkIYVEPb89m6Am1Jqr1LqQJoacXHBnvpPAf6ulDqPdXbjKwVj2gPB3T4b7poiIXOh\nyR6l1N+BJkCbwralIFBKlQI+BgYXsimFSWmsQ0htsfYQdyulvETkWqFaVXA8D3whIh8ppZpjXevk\nKSKWwjasOPCg9hTuRiKDnCQyiij21B+lVEfgXaCniCQWkG35TW51fxjwBEKVUjFYx1U3FqNgsz2/\n/Xlgo4gki8gZ4CRWJ1EcsKf+LwCrAURkP1AWqy5QScCuZ8P98KA6hZIukZFr/ZVSjYBFWB1CcRpT\nzrHuInJdRKqKSB0RqYM1ntJTRA4Vjrl5jj1/++ux9hJQSlXFOpz0e0EamY/YU/9YoAOAUqohVqdQ\nUrbx2wgMSpuF1Ay4LiIX8/IGD+TwkZRwiQw76z8LqACsSYuvx4pIz0IzOo+ws+7FFjvrvwXorJQ6\nDqQCb4pIsegl21n/scC/lVJjsAadBxeXF0KlVDBWh181LWYyGXAEEJFPscZQugGngARgSJ7bUEza\nUqPRaDR5wIM6fKTRaDSaQkA7BY1Go9EYaKeg0Wg0GgPtFDQajUZjoJ2CRqPRaAy0U9Bo7kAplaqU\nilRKHVVKbVJKVcrj8gcrpT5J+z5FKfVGXpav0dwP2iloNLbcEhGziHhiXQPzcmEbpNEUFNopaDQ5\ns58MgmNKqTeVUgfTtOzfy5A+KC3tZ6XU8rS0p9P2+vhJKbVdKfVoIdiv0dwVD+SKZo3mQUAp5YBV\nTmFJ2nFnrBpDTbHq2W9USrXGqrk1AfAVkb+UUpXTitgDNBMRUUoNA8ZhXY2r0TywaKeg0djipJSK\nxNpDOAFsS0vvnPb5Ke24AlYn4Q2sEZG/AEQkXQ+/JrAqTe++DHCmYMzXaO4dPXyk0dhyS0TMgDPW\nHkF6TEEBH6TFG8wi8oSILMmhnHnAJyLiBbyIVbhNo3mg0U5Bo8mGtJ3tXgXGpsmzbwGGKqUqACil\naiilqgM7gWeVUlXS0tOHjx7hf7LGgWg0RQA9fKTR5ICI/KSUigKeF5HlaVLN+9OUaW8Af09T8Xwf\nCFNKpWIdXhqMdYewNUqpq1gdR93CqINGczdolVSNRqPRGOjhI41Go9EYaKeg0Wg0GgPtFDQajUZj\noJ2CRqPRaAy0U9BoNBqNgXYKGo1GozHQTkGj0Wg0BtopaDQajcbg/wEJBcQ4a2rvYQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa69703c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def thresh_dets(dets, thresh):\n",
    "    thresholded = {}\n",
    "    for name in dets:\n",
    "        thresholded[name] = [d for d in dets[name] if d.confidence > 0.4]\n",
    "        \n",
    "    return thresholded\n",
    "\n",
    "thresh = 0.5\n",
    "    \n",
    "teddy_t = bbb.pr(thresh_dets(patch_simen, thresh), annotations)['person']\n",
    "up_t = bbb.pr(thresh_dets(patch_up, thresh), annotations)['person']\n",
    "noise_t = bbb.pr(thresh_dets(noise_results, thresh), annotations)['person']\n",
    "clean_t = bbb.pr(thresh_dets(clean_results, thresh), annotations)['person']\n",
    "class_shift_t = bbb.pr(thresh_dets(class_results, thresh), annotations)['person']\n",
    "class_only_t = bbb.pr(thresh_dets(class_only, thresh), annotations)['person']\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "\n",
    "ap = bbb.ap(clean_t[0], clean_t[1])\n",
    "plt.plot(clean_t[1], clean_t[0], label=f'Original: AP: {round(ap*100, 2)}%, recall: {round(clean_t[1][-1]*100, 2)}%')\n",
    "\n",
    "ap = bbb.ap(class_shift_t[0], class_shift_t[1])\n",
    "plt.plot(class_shift_t[1], class_shift_t[0], label=f'OBJ-CLS: AP: {round(ap*100, 2)}%, recall: {round(class_shift_t[1][-1]*100, 2)}%')\n",
    "\n",
    "ap = bbb.ap(noise_t[0], noise_t[1])\n",
    "plt.plot(noise_t[1], noise_t[0], label=f'NOISE: AP: {round(ap*100, 2)}%, recall: {round(noise_t[1][-1]*100, 2)}%')\n",
    "         \n",
    "ap = bbb.ap(up_t[0], up_t[1])\n",
    "plt.plot(up_t[1], up_t[0], label=f'OBJ: mAP: {round(ap*100, 2)}%, recall: {round(up_t[1][-1]*100, 2)}%')\n",
    "\n",
    "ap = bbb.ap(class_only_t[0], class_only_t[1])\n",
    "plt.plot(class_only_t[1], class_only_t[0], label=f'CLS: AP: {round(ap*100, 2)}%, recall: {round(class_only_t[1][-1]*100, 2)}%')\n",
    "\n",
    "plt.gcf().suptitle('PR-curve')\n",
    "plt.gca().set_ylabel('Precision')\n",
    "plt.gca().set_xlabel('Recall')\n",
    "plt.gca().set_xlim([0, 1.05])\n",
    "plt.gca().set_ylim([0, 1.05])\n",
    "plt.gca().legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
